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Peace, Terrorism, or Civil Conflict?Understanding the Decision of an Opposition Group

IZA DP No. 9996

June 2016

Michael Jetter

Peace, Terrorism, or Civil Conflict? Understanding the Decision of an

Opposition Group

Michael Jetter University of Western Australia

and IZA

Discussion Paper No. 9996 June 2016

IZA

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IZA Discussion Paper No. 9996 June 2016

ABSTRACT

Peace, Terrorism, or Civil Conflict? Understanding the Decision of an Opposition Group*

When do opposition groups decide to mount a terrorism campaign and when do they enter an open civil conflict against the ruling government? This paper models an opposition group’s choice between peace, terrorism, and open conflict. Terrorism emerges if executive constraints are intermediate and rents are sizeable. Open conflict is predicted to emerge under poor executive constraints. Analyzing country-level panel data firmly supports these hypotheses, even when relying on within-country variation only in a fixed-effects framework. In particular, both the incidence of terrorism and the likelihood of terrorism onset increase under intermediate executive constraints (following an inverted U-shape) and if large rents are available from natural resources, oil, or foreign development assistance. A one-standard-deviation increase in rents raises casualties by approximately 15 percentage points. Related to civil conflict, moving from an authoritarian regime to comprehensive executive constraints is associated with a decrease in the number of battle-related deaths by approximately 74 percentage points. These findings can help us to better understand and anticipate the underlying decision of opposition groups and their choice between peace, a terrorism campaign, and open conflict. JEL Classification: D74, F35, O11, P47, Q34 Keywords: conflict, executive constraints, foreign aid, natural resource rents, oil rents,

political institutions, rents, terrorism Corresponding author: Michael Jetter Business School University of Western Australia 35 Stirling Highway Crawley, WA 6009 Australia E-mail: [email protected]

* I am grateful to Julia Debski for outstanding research assistance in this project.

1 Introduction

We continue to observe large-scale organized violence against ruling governments in many coun-

tries around the world. Sometimes, discontent materializes in an open insurgency against the

state, and other times domestic terrorism emerges. These phenomena are usually analyzed sep-

arately.1 In reality, however, an opposition group consciously decides about which (if any) form

of organized violence to pursue against a ruling government. Then why do some groups choose

an open insurgency against their government and others pursue a concealed strategy of domestic

terrorism?

Understanding the underlying drivers of large-scale organized violence against the state has

become more important than ever, as casualties are on the rise again. Figure 1 visualizes the

number of annual deaths from domestic conflicts and terrorism – both cracked record-highs in

2014 surpassing 100,000 and 42,000 casualties, respectively.

020

000

4000

060

000

8000

010

0000

Num

ber

of c

asua

lties

1970 1980 1990 2000 2010Year

Terrorism Conflicts

Figure 1: Worldwide casualties from terrorism and conflicts, using data from the UppsalaConflict Data Program (UCDP) and the Global Terrorism Database (GTD).

The following pages first introduce a basic theoretical intuition about how an opposition

group chooses its profit-maximizing strategy between (i) peace, (ii) terrorism, and (iii) open

insurgency. Although terrorism and conflicts share a number of common characteristics, the

1A notable exception is provided by Findley and Young (2012).

1

paper highlights an important distinction: whereas the potential gains and costs are large in open

insurgencies, both are naturally limited in terrorist campaigns. Two testable results emerge, as

the opposition’s choice depends on constraints on the executive and the available rents (e.g.,

natural resource rents, oil, or foreign development assistance), among other, mostly country-

specific parameters.

First, terrorism becomes likely if political constraints are intermediate and rents are sizeable.

Intuitively, the opposition does not want to risk losing its non-trivial share of rents in open

conflict, but the looming benefits from a terrorist campaign are more attractive than peace. In

reality, we have observed such scenarios in Algeria (Armed Islamic Group and Al-Qa’ida in the

lands of the Islamic Maghreb) over the past 20 years and in Nigeria (Boko Haram), for example.2

Interestingly, terrorism can emerge even under near-perfect institutions if rents are particularly

high.

Second, open conflict emerges if constraints on the executive are poor. In this case, the oppo-

sition has little to lose from mounting an open insurgency and rewards from a victorious uprising

are looming large. As examples, one may consider a number of domestic conflicts in Africa, such

as the Ethiopian civil war (1974 – 1991). Finally, peace prevails if (i) executive constraints

are sufficiently large and rents are moderate or (ii) executive constraints are well developed,

in which case the size of rents becomes irrelevant. Examples may be found in Scandinavian

nations, Australia, or New Zealand, among many others.

To test these hypotheses, I analyze country-year level data on casualties from terrorism

and domestic conflicts. Applying a two-way fixed-effects framework allows me to focus on

within-country variation only, controlling for any unobservable heterogeneity across countries

and time. Indeed, terrorism most likely occurs if a country exhibits an intermediate degree of

executive constraints and high rents, measured as natural resources (in particular oil) and foreign

development assistance. This quantitatively sizeable result holds for both the incidence and

onset of terrorism. In terms of magnitude, moving from an authoritarian regime to intermediate

political constraints translates to a 40 percentage point increase in casualties from terrorism.

2Algeria has maintained intermediate executive controls over the past 20 years with values ranging from threeto five on the Polity IV variable xconst (scale ranging from one to seven). The country also enjoys rents fromlarge oil and gas reserves, which it exports (see CIA, 2016). A similar scenario applies to Nigeria with executiveconstraints equivalent to a value of five since 1999 and sizeable oil exports.

2

Similarly, the likelihood of terrorism onset increases by approximately seven percentage points.

Consistent with the model, conflict intensity decreases linearly as institutional constraints on

the ruling elite improve. Moving from a completely authoritarian regime to inclusive institutions

is associated with a decrease in the number of deaths from domestic conflict by more than 74

percentage points. This relationship remains linear, as predicted by the model and confirms

previous results in the literature (see Hegre, 2014, for an overview).

The paper aims to contribute to two major streams in political science and political economy.

First, it provides a basic unifying framework to study terrorism and domestic conflict jointly.

This can help us understand (and potentially anticipate) why we observe terrorism in some

countries and open insurgency in others. It also explains why transitioning democracies can

become vulnerable to terrorism, namely if substantial rents are available. The simple theoretical

intuition builds on foundations from Besley and Persson (2009, 2011), who analyze political

violence from the government’s perspective, whereas this paper focuses on the choice of the

opposition group.

Second, the paper adds to empirical works on the determinants of terrorism and domestic

conflict. Terrorism can indeed be more likely in relatively democratic societies, as pointed

out by Chenoweth (2013), even after country-specific heterogeneity is accounted for. When

it comes to terrorism, intermediate constraints on the executive and sizeable rents can prove

a dangerous combination. Related to the civil conflict/civil war literature, the corresponding

findings highlight the overwhelming importance of institutional constraints, as previously argued

by Hegre (2014). The qualitative and quantitative interpretations of the derived findings are

considerable, both for the incidence and onset of terrorism and conflict.

The paper proceeds with a description of the literature, aimed at sorting this paper into

existing theories and empirical findings. Section 3 introduces a simple theoretical model of a

profit-maximizing opposition group, whereas section 4 presents the data and empirical method-

ology. Sections 5 and 6 analyze the empirical implications of the underlying hypotheses for

terrorism and civil conflict. Finally, section 7 concludes.

3

2 Background

Domestic terrorism and open insurgencies share many similarities as organized forms of political

violence. Blattman and Miguel (2010, p.6) point out that “the distinction between civil wars and

other forms of political instability has largely been assumed rather than demonstrated.” More

specifically, Lessing (2015) highlights the need to distinguish between forms of organized violence

that are intended to take control of the government, as opposed to those that carry other goals.

To clarify terminology, consider the respective definitions provided by the Merriam-Webster

dictionary:

• Terrorism: the use of violent acts to frighten the people in an area as a way of trying to

achieve a political goal.

• Insurgency: a usually violent attempt to take control of a government: a rebellion or

uprising.3

The uniting theme across these concepts centers on the notion of organized violence, usually

against a ruling government. At its roots, both concepts constitute expressions of a group’s

deep dissatisfaction with the status quo, leading them to choose violent means with the goal

of changing political institutions. However, a terrorist campaign and an open insurgency differ

along some relevant dimensions. Most importantly, terrorism is motivated by achieving a political

goal (generally speaking to obtain more political power), whereas the purpose of mounting an

insurgency lies in overthrowing the government completely.4

In economic terms, the costs and benefits differ between the concepts of terrorism and insur-

gency. The benefits from an open insurgency are larger (control of the government), but so are

the associated costs as an insurgency consists in open fighting against a ruling government. This

openness is expressed as a group’s public declaration of violent government opposition, which

in turn legitimizes the government’s persecution of all group members. As a consequence, the

group may lose its institutional privileges (little as they may have been) and become outlaws.

3Another term, virtually analogous to an insurgency relates to a political coup (or coup d’etat), defined as “asudden, violent, and illegal seizure of power from a government.”

4In practice, more political power may translate to regional independence, a more equal distribution of re-sources, or concessions in the country’s institutional framework.

4

In terrorism, on the other hand, resistance is usually organized underground, actors are

hidden, and the costs associated with terrorist attacks can be manageable, especially when com-

pared to mounting an insurgency. Usually, group members do not publicly identify with the

organization’s political goals and in the worst-case scenario those members conducting terrorist

missions will lose their institutionally guaranteed rights or die. The secret character of terror-

ism produces a natural, manageable limit to campaign costs. These distinctions between an

insurgency and terrorism will become important in section 3.

Previous works on the determinants of domestic conflicts usually distinguish between eco-

nomic (e.g., income levels) and political drivers (e.g., political rights or democracy).5 Some

studies have identified the presence of natural resources as a potential factor (e.g., see Collier

and Hoeffler, 1998, and Cotet and Tsui, 2013), as well as foreign aid inflows (e.g., see Nielsen

et al., 2011, and Nunn and Qian, 2014). The present article adds to these studies in highlighting

the importance of political constraints and rents from resources or international assistance in

explaining organized violence. In particular, the paper provides an explanation for the question

why some groups may choose terrorism over peace or open conflict.

Similar to conflict studies, the terrorism literature has identified some key drivers, such as de-

velopment levels (mostly GDP per capita) and political rights in several forms (e.g., democracy,

political freedom, civil liberties, or the rule of law).6 One particularly controversial observation

suggests that democratic states can become targets of terrorism – a phenomenon we do not

observe for domestic conflicts usually (see Hegre, 2014). Chenoweth (2013) writes that “tran-

sitioning democracies with internally inconsistent institutions were more likely to experience

5In a series of seminal papers, Paul Collier and Anke Hoeffler distinguish between greed and grievance, sug-gesting economic opportunity as the key driver of civil wars (Collier and Hoeffler, 1998, 2004; Collier et al., 2009).Miguel et al. (2004) show that higher income can alleviate conflict, using an instrumental variable approach basedon rainfall in Africa. Blattman and Miguel (2010) provide a comprehensive overview of the existing literature oncivil war. Dixon (2009) focuses on summarizing the empirical literature on the determinants of civil war onset.Further, the demographic composition of society, in particular ethnic fractionalization and polarization, has beenhighlighted in a number of influential papers, in particular by Fearon and Laitin (2003), Reynal-Querol and Mon-talvo (2005), and Joan Esteban and Debraj Ray (Esteban and Ray, 2008; Esteban and Ray, 2011; Esteban et al.,2012). In the present paper, ethnic components will not be the focus and will be assumed to remain constantwithin a country over time.

6The potential link between income levels and terrorism has received mixed evidence. Krueger and Maleckova(2003), Blomberg et al. (2004), Abadie (2006), and Enders and Hoover (2012) provide important studies. Notethat the present paper focuses on domestic terrorism, not international terrorism. In reality, only 3.8 percentof the documented terrorist attacks in the GTD are categorized as international terrorism. These missions areexcluded from the empirical analysis.

5

domestic terrorism than advanced democracies and authoritarian regimes.”

This paper provides an intuitive explanation for this observation. If constraints on the

executive are intermediate (as they are in countries moving from an authoritarian regime to

democracy) terrorism may emerge as the profit-maximizing choice of the opposition group. In

fact, even if institutional constraints are considerable, terrorism can be observed, but only if

rents are particularly large. These hypotheses emerge directly from the cost-benefit distinctions

between open conflict and concealed terrorism.

3 Modelling the Opposition’s Choice

Assume an opposition group, normalized to the size of one. To keep things simple, the size

of the opposition group equals the size of the ruling government group (akin to Besley and

Persson, 2009, 2011). Similarly, no within-group coordination problems are permitted, although

one could amend the decision process with such dynamics without loss of generality. Given the

status quo, which will be introduced shortly, the opposition can choose between one of three

strategies: peace, terrorism, or open insurgency. The decision process is modeled in its simplest

form as one static period for a risk-neutral opposition group, in order to emphasize the basic

underlying problem.

Following seminal models by Timothy Besley and Torsten Persson (2009 and 2011), the

country’s institutional foundations guarantee the opposition group a minimum share σ (with

0 ≤ σ ≤ 12) of the available rents, R. (It is straightforward to show that a ruling government

will always choose the lowest σ possible in the following framework. See the appendix for details.)

σ can be interpreted as constraints on the executive or, alternatively, as extractive (low σ) versus

inclusive institutions (high σ), following the terminology used by Acemoglu et al. (2005). In

the spirit of Besley and Persson (2009, 2011), R refers to natural resource rents or foreign aid

inflows – both assets over which a ruling government maintains control.

In reality, we observe governments that extract parts of these available rents in many coun-

tries. In the given setup, 1− σ represents the maximum share of R the ruling government can

extract. If σ = 12 , then rents are shared equally between both equally-sized groups and insti-

tutions are completely inclusive, i.e., the ruling elite does not extract any rents beyond their

6

proportional share. Contemporary examples are provided by a number of largely democratic

states, such as the majority of European states, North America, Australia, or New Zealand.

In the other extreme (σ = 0), the ruling group extracts all rents, leaving none for the

opposition. Examples can be found in strict authoritarian regimes, such as North Korea, Fidel

Castro’s Cuba, or Robert Mugabe’s Zimbabwe. In reality, most regimes fall somewhere in the

middle with 0 < σ < 12 .

3.1 Peace

The opposition group’s first option is characterized by non-violence. In particular, maintaining

peace will yield the opposition group a profit of

Πpeace = σR. (1)

Notice that if constraints on the executive are optimal and σ = 12 , then both groups benefit

equally from revenues, since the ruling group’s revenue remains (1−σ)R in this basic framework.

If σ = 0, however, no institutional restraints are posed on the executive and the reigning group

can reap all resource revenue, equivalent to R.

3.2 Terrorism

Now consider the second option: a terrorist campaign. The idea of forming a terrorist movement

consists in using organized violence to enforce better institutional terms, i.e., an even distribution

of the available rents, corresponding to σ = 12 . We can think of a number of potential demands

that can be summarized under σ, such as territorial concessions (e.g., separatist groups) or

improved political and economic power. However, it is not possible to take control of the

government with terrorist tactics, naturally limiting the potential gains of a terrorist campaign.

In turn, the costs of terrorism are determined by a fixed non-negative amount c for two

main reasons. First, mounting a terrorist campaign means that no fractional member of the

opposition needs to openly declare herself as violently opposed to the government. Terrorism

implies secrecy. Thus, accountability is limited by the concealed nature of terrorism. Second, in

reality, conducting terrorist attacks can be as simple as one individual firing a gun in a highly

7

populated area. This aspect of terrorism drastically limits the associated costs, especially when

compared to open insurgency.

Both of these characteristics capture the fundamental difference between terrorism and in-

surgencies. In general, the cost of terrorist attacks remains relatively small across countries and

time, although country- and time-specific aspects are likely to influence c.7 In the empirical

framework, fixed effects will be introduced to capture such unobservable heterogeneity across

countries and time. Further, if β (with 0 < β < 1) represents the probability of success, the

expected profit of mounting a terrorist campaign becomes8

Πterror = σR+ β(1

2− σ)R− c. (2)

In practice, β can include a number of country-specific aspects that may favor or complicate a

successful campaign of violence against the government. For instance, if the state’s institutions

are weak, the chances of a successful revolution increase (e.g., see discussion in Besley and

Persson, 2011). As another example, geographical aspects could facilitate or complicate the

chances of a successful revolution (e.g., see Abadie, 2006, for terrorism; Fearon and Laitin, 2003,

Collier et al., 2009, Do and Iyer, 2010, Weidmann and Ward, 2010, or Schutte, 2015, for conflict).

3.3 Insurgency

Finally, the opposition group can consider a third option: open insurgency. In this case, potential

gains are higher than from choosing a terrorist campaign, as the looming reward consists of taking

over the government and reaping (1 − σ)R. However, the costs of mounting an insurgency are

also non-trivial and amount to devoting all available resources into fighting (σR), as the group

declares an open war on the ruling regime. Another interpretation of the associated costs

amounting to σR relates to the notion that once a group declares war on the government it is

not eligible to receive its institutional share of rents anymore. Thus, the payoff from insurgency

becomes

7For example, we can think of technological advancements over time, country-specific government surveillance,or even within-group coordination problems as drivers of c.

8To be entirely accurate, this payoff function is an expected payoff function. However, since I am assuming arisk-neutral opposition group this distinction between expected and actual profit becomes irrelevant in the presentcontext.

8

Πwar = σR+ β(1− σ)R− σR. (3)

Note that this setup implies two simplifying assumptions. First, the probability of a suc-

cessful terrorist campaign is equalized to the probability of a successful insurgency. In reality,

of course, these odds may differ for several reasons and βterror = βwar = β is chosen for math-

ematical convenience. Second, an insurgency (and likely terrorism as well) may destroy rents,

potentially reducing R. Nevertheless, altering either assumption would not change the derived

qualitative implications, but would complicate calculations. The general intuition of this mod-

eling is preserved for βterror 6= βwar and for introducing an additional cost term measuring

destruction from war. In the empirical section, country- and time-fixed effects are employed to

control for such heterogeneity within countries and time periods.

3.4 Optimal Choice of the Opposition

Which option will the opposition group choose? First, it is useful to review corner solutions,

i.e., cases where institutional constraints are completely absent or ideal. In reality, it is highly

unlikely that the group will choose insurgency if σ → 12 , as we rarely observe attempts aimed at

overthrowing the government in societies with inclusive institutions (see Hegre, 2014). Indeed,

β < 1 is sufficient to ensure Πpeace > Πwar for σ = 12 .

In addition, it is realistic to assume that at least one violent option dominates the peaceful

scenario if σ = 0. Note that equation 3 by definition fulfills that restriction, whereas

R >2c

β(4)

would be required for terrorism to even be considered. Intuitively, if available rents are suf-

ficiently small, terrorism does not present itself as a lucrative option at all. Similarly, if the

associated costs (c) of mounting a terrorist campaign are large, terrorism becomes a less desir-

able alternative. Finally, if the probability of success (β) approaches zero, terrorism loses its

attractiveness for the opposition.9

9In fact, the idea of not negotiating with terrorists aims to reduce βterror to zero, signaling that terrorist actswould by no means be considered in the political process. However, governments have frequently broken thatideal. For more detailed analyses, see Clutterbuck (1992), Neumann (2007), and Dolnik and Fitzgerald (2011).

9

Comparing payoffs, we can then derive the conditions under which each option is preferable.

Figure 2 visualizes the potential scenarios, graphing the constraints on the executive (σ) to the

available rents (R). In particular, we can establish the following optimal strategies of violence:

Proposition 1. If executive constraints are intermediate (with cR + 1

2β < σ < 12 −

cβR)

and rents are non-trivial (with R > 2c(1+β)β(1−β) ), terrorism becomes an attractive option

for the opposition group.

Proof. Follows directly from comparing equation 2 to equations 1 and 3.

Figure 2: Predicted occurrence of war, terrorism, and peace under different combinations ofexecutive constraints and available rents.

Comparing terrorism to peace, even if σ approaches 12 an opposition may find it beneficial to

promote terrorism, but only if rents are particularly large. This may explain why even relatively

10

advanced democracies can suffer from domestic terrorism. Comparing terrorism to an insurgency

suggests that if constraints are exceptionally poor, then the opposition has little to lose from

an open conflict. On the other hand, if σ is moderate already, then seeking a larger share of R

via terrorist measures can prove to be more beneficial, as the potential losses from an open war

loom too large.

Proposition 2. If executive constraints are small (with σ < β1+β and σ < c

R + 12β)

insurgency becomes likely.

Intuitively, the decision between peace and insurgency is simple: if the share of rents received

is exceptionally low, then an opposition group has little to lose from mounting an insurgency.

This becomes more likely if the state’s power is weak (implying a high β). This explains why

countries with weak institutions struggle to maintain peace – the chances for a successful rev-

olution (β) are just too attractive and usually executive constraints are too weak (i.e., a small

σ). These hypotheses have received firm support in the empirical literature (e.g., see Hegre and

Sambanis, 2006, and Blattman and Miguel, 2010, for comprehensive summaries).

4 Data and Empirical Methodology

4.1 Data

The literature generally considers three aspects of violent conflicts: incidence, onset, and du-

ration (see Blattman and Miguel, 2010, for a detailed discussion). Naturally, the provided

theoretical intuition is most applicable to the incidence and onset of terrorism and conflict, as

studies on the duration of such events are likely to follow different dynamics (e.g., see Acemoglu

et al., 2010, and Acemoglu and Wolitzky, 2014, for recent theoretical works). Thus, the empirical

section will focus on studying incidence and onset of terrorism, followed by the same sequence

for civil conflict.

To test Propositions 1 and 2, I access the GTD (introduced by LaFree and Dugan, 2007) and

the UCDP (UCDP, 2015) for detailed data on deaths from terrorist attacks and internal conflicts.

The GTD (START, 2015) defines terrorism as “the threatened or actual use of illegal force and

violence by a non-state actor to attain a political, economic, religious, or social goal through fear,

11

coercion, or intimidation.” In particular, I will focus on the number of casualties from terrorism.

(Nevertheless, all derived results are consistent when using the number of attacks.) In turn,

the UCDP (UCDP, 2015) defines armed conflict as “a contested incompatibility that concerns

government and/or territory where the use of armed force between two parties, of which at least

one is the government of a state, results in at least 25 battle-related deaths.”

The GTD contains information on terrorist attacks from 1970 to 2014 and I aggregate that

information to the country-year level.10 The UCDP battle-related deaths dataset provides infor-

mation on the number of casualties from internal and internationalized internal conflicts on the

country-year level from 1989 to 2014. Both data sources have become standard in the respective

literature. Table AII lists all sample countries with their respective number of observations for

both samples.

Note that the literature generally refers to conflicts with more than 1,000 battle-related

deaths in a given year as civil wars (e.g., see Blattman and Miguel, 2010). If a threshold of 25

battle-related deaths has been crossed, then researchers refer to civil conflict. Most datasets only

offer binary indicators for civil conflict and civil war. However, the UCDP battle-related deaths

dataset allows for a much more continuous measure of an open and violent opposition to the

government, providing the number of casualties. Nevertheless, all results are consistent when

using a binary indicator for more than 25 casualties as the dependent variable for terrorism and

conflict. The corresponding findings are referred to Table AV in the appendix.

To measure σ, the institutional constraints on the executive, I access the Polity IV dataset, a

common source for political variables (introduced by Marshall and Jaggers, 2002). In particular,

I use the variable xconst (executive constraints, labeled EXEC from hereon), ranging from one

to seven, where larger values symbolize tighter constraints on the executive. In alternative

estimations, I also employ the polity2 variable capturing a country’s degree of democracy.

Further, measures forR (natural resource rents, foreign development assistance, and oil rents)

are collected from the World Development Indicators (Group, 2012). Finally, additional control

variables are taken from conventional sources for country-level data and will be introduced in the

upcoming subsection. Summary statistics of all variables are referred to the Appendix Tables

10The only missing year is 1993, in which the GTD does not provide data because of missing files. Note thatI exclude international terrorism and inter-country conflicts, as these phenomena are likely following differentdynamics than the domestic situation described in this paper.

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AIII and AIV.

4.2 Empirical Methodology

4.2.1 The Incidence of Terrorism and Conflict

Analyzing the incidence of terrorism, I estimate the following regression for country i and year

t, before employing the same structure to estimate the incidence of conflicts.

Ln(1 + deaths) = α1EXECit + α2

(EXEC)2

it + α3Rentsit + Xitα4 + δi + ρt + κit + εit. (5)

Note that the dependent variable is calculated as Ln(1+deaths), which conserves country-year

observations where no deaths occurred. In the case of terrorism, we would expect α1 to exhibit

a positive coefficient, whereas α2 is predicted to be negative, corresponding to the notion that

terrorism is most likely to occur in societies with intermediate controls on the executive. For

conflicts, we would expect α1 to exhibit a negative coefficient and α2 should be statistically

irrelevant.

The effect of available rents is captured by α3 and in the case of terrorism we predict a

positive relationship. In particular, I will consider natural resource rents, oil rents, and foreign

development assistance as measures for the available rents (akin to Besley and Persson, 2009,

2011). Following the theoretical motivation, resource rents are expected to be less of a factor in

driving conflict.

To control for potentially confounding characteristics that may independently influence the

occurrence of large-scale organized violence against the state, the vector Xit incorporates the

conventional time-variant control variables. In particular, I include GDP per capita, population

size (employing the natural logarithm for both), and the rate of economic growth. These factors

have emerged as likely drivers of organized violence in the associated literature.11

δi and ρt constitute country- and year-fixed effects, whereas κit incorporates continent-

specific time trends. Note that country-fixed effects are absorbing any time-invariant country-

11For the importance of income and population size in explaining conflicts, see Collier and Hoeffler (1998),Fearon and Laitin (2003), or Cotet and Tsui (2013). Blomberg et al. (2004) and Enders and Hoover (2012)highlight the role of income levels in explaining terrorism. Blomberg et al. (2004) and Miguel et al. (2004) findgrowth rates to matter for terrorism and conflicts, respectively.

13

specific factors that could be associated with c and β. This captures geographical aspects,

colonial origin, individual history, and other time-invariant heterogeneity on the country level.

Fixed effects also reasonably control for characteristics that only change slowly over time, such

as ethnic shares or religious distributions. Thus, fixed effects are alleviating concerns about

omitted variables.

In addition, fixed effects provide a reasonable assurance against endogeneity concerns from

measurement error. For example, if data quality in certain (potentially less developed) countries

or specific timeframes is imprecise, a fixed-effects framework would capture such shortcomings.

In general, several topics of interest in the cross-country literature have encountered the im-

portance of using a fixed-effects framework to contain endogeneity concerns in a powerful way.

Examples can be found in the analysis of economic growth (see Islam, 1995) or democracy

(see Acemoglu et al., 2008, and Cervellati et al., 2014). In the case of understanding conflict

drivers, Cotet and Tsui (2013) have shown the importance of using panel data with fixed ef-

fects. Continent-specific time trends incorporate the idea that developments related to conflict

or terrorism can sometimes spill over into neighboring countries.12 The Arab Spring provides

a recent popular example. Finally, εit stands for the conventional error term, clustered on the

country level.

Beyond the incidence measures, the empirical analysis then turns to analyzing the onset of

terrorism and conflict. I first calculate a binary dependent variable that takes on the value of

one if a country suffers deaths from terrorism in a given year, but has not experienced such

deaths in the previous year. Measuring conflict onset follows the same logic.

Applying probit regressions allows me to estimate the influence of executive constraints and

rents on the likelihood of terrorism and conflict onset. As independent variables, I incorporate

the same regressors as in equation 5, excluding fixed effects and time trends. In practice, the

onset of both terrorism and conflict are (thankfully) much rarer within countries over time than

the variation in the incidence variable of casualties. As a consequence, a fixed-effects framework

would not leave sufficient statistical variation in the data to reveal the underlying relationships.

12In alternative estimations, I also incorporate country-specific time trends, producing a much tighter econo-metric framework. In these estimations, results are consistent with the displayed results in terms of suggestedsigns and magnitudes. In few estimations, the level of statistical significance decreases for some covariates. How-ever, this is to be expected, as time variation within a country is limited in a number of control variables. Thus,introducing country-specific time trends can absorb much of the underlying variation.

14

In fact, the average sample country incurs approximately 3.5 terrorism onset years and 0.64

conflict onset years. These aspects will be discussed in more detail as the corresponding results

are presented.

Finally, results from several alternative specifications will be presented for each estimation,

focusing on alternative measures for the key variables, as well as addressing potential endogeneity

concerns.

5 Empirical Analysis of Terrorism

This section discusses the empirical findings related to the incidence and onset of terrorism,

including alternative estimations. The same structure follows for the incidence and onset of

conflicts.

5.1 Incidence of Terrorism: Main Results

Table 1 considers the incidence of terrorism, measured by the number of casualties from terrorism

in country i and year t. In the first column, only a linear term of institutional constraints is

used as an explanatory variable. The derived coefficient is positive, but not relevant on any

conventional level of statistical significance. Were we to stop here, we would conclude that

executive constraints are unrelated to the incidence of terrorism.

Column (2) then acknowledges the nonlinearity suggested by the theoretical intuition. In-

deed, we find strong evidence for a quadratic shape and the respective coefficients are both

significant on the one percent level. As constraints on the executive strengthen, terrorism is

suggested to rise at first and then fall after peaking at a value of 4.3 on a scale of one to seven.

Column (3) includes country-fixed effects, thereby controlling for individual particularities of

each state. In the context of the basic model presented before, this controls for (but is not limited

to) the cost of conducting a terrorist attack (c) and the probability of a successful campaign

(β). It is interesting to see that the coefficients associated with institutional constraints only

change marginally, even though the explanatory power of the model increases substantially from

an adjusted R2 of 0.015 to explaining over 45 percent of the variation in the occurrence of

terrorism.

15

Table 1: OLS regression results, estimating the number of deaths from terrorism in country iand year t.

(1) (2) (3) (4) (5) (6)

Dependent variable: Ln(1+deaths from terrorism)

EXEC 0.017 0.508∗∗∗ 0.588∗∗∗ 0.372∗∗ 0.366∗∗ 0.308∗

(0.033) (0.166) (0.187) (0.178) (0.176) (0.164)

(EXEC)2 -0.059∗∗∗ -0.066∗∗∗ -0.047∗∗ -0.045∗∗ -0.036∗

(0.021) (0.023) (0.023) (0.022) (0.020)

Natural resource rents 0.485∗∗ 0.507∗∗ 0.669∗∗∗

in US$ 10,000/cap (0.236) (0.214) (0.233)

Country-fixed effects yes yes yes yes

Control variablesa yes yes yes

Year-fixed effects yes yes

Continent-specific time trends yes

# of countries 158 158 158 158 158 158N 5,400 5,400 5,400 5,400 5,400 5,400Adjusted R2 0.000 0.015 0.452 0.467 0.498 0.530

Notes: Standard errors clustered on the country level are displayed in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗

p < 0.01. aIncludes GDP/capita, population size, and growth rate.

16

Column (4) incorporates further control variables, in particular GDP per capita, population

size, the economic growth rate, and finally natural resource rents. Recall that natural resource

rents correspond to R in the model and we would expect a positive coefficient here. Indeed, this

hypothesis is supported.

Finally, adding year-fixed effects and continent-specific time trends provides a much tighter

econometric framework. In the most complete estimation, both underlying hypotheses are con-

firmed: constraints on the executive remain non-linear in predicting terrorism with a maximum

for intermediate ranges of institutional constraints, whereas higher natural resource rents are

associated with more terrorism.

In terms of magnitude, both results are non-trivial, as visualized in Figure 3. In partic-

ular, terrorism peaks at a value of 4.3 on the scale of executive constraints. Relative to a

completely authoritarian regime (value of one), the average number of deaths from terrorism is

approximately 40 percentage points higher in the case of intermediate institutional constraints.

Note that terrorism in largely inclusive institutions, corresponding to a value of σ close to 0.5,

still remains more prevalent than in authoritarian regimes. This finding is consistent with the

model’s predictions. Related to R, increasing natural resource rents by one standard deviation

(US$ 2,426 per capita) corresponds to a 16.2 percentage point increase in the casualties from

terrorism.

3040

5060

70%

cha

nge

in c

asua

lties

from

terr

oris

m

0 2 4 6 8Constraints on executive

020

4060

80%

cha

nge

in c

asua

lties

from

terr

oris

m

0 .2 .4 .6 .8 1Natural resource rents in $10,000 per capita

Figure 3: Effect of constraints on the executive and natural resource rents on the incidence ofterrorism, plotting results from column (6) of Table 1.

17

5.2 Incidence of Terrorism: Extensions

From these baseline findings related to the incidence of terrorism, I now consider several ex-

tensions, displayed in Table 2. Columns (1) and (2) turn to alternative measures for rents,

namely foreign development assistance and oil rents. In the spirit of Besley and Persson (2009,

2011), rents may relate to natural resources or foreign assistance – both of which are likely

at the disposal of a ruling government. First, including development assistance produces the

expected result, as larger inflows are associated with more terrorism. In terms of magnitude,

a one standard deviation increase in foreign assistance (US$ 117 per capita) corresponds to a

14.9 percentage point increase in the number of terrorism casualties. Throughout the additional

estimations in Table 2, this result remains remarkably stable.

Second, with respect to specific natural resources, column (2) supports the idea that larger

oil revenues directly correspond to more terrorism. (Note that to avoid multicollinearity issues,

I remove the measure for natural resource rents once oil rents are included. These variables

are highly correlated with a coefficient of 0.97.) In this case, a one standard deviation increase

(US$ 2,078) is associated with a 15.8 percentage point increase in the number of deaths from

terrorism.

Reminding ourselves of the previous coefficients associated with a one standard deviation

increase in natural resources (16.2 percentage points) or development assistance (14.9 percentage

points), these estimates are remarkably close. Thus, a general relationship between R and ter-

rorism appears likely, as suggested by Proposition 1. In addition, the non-linear result associated

with institutional constraints remains robust to these alternative estimations.

Focusing on the measure of institutional constraints, column (3) introduces an alternative

measure with the polity2 variable of democracy, provided by the Polity IV project. In order to

properly estimate the quadratic effect, I re-scale the initial polity2 variable to all positive values

ranging from zero (corresponding to total autocracy) to 20 (total democracy). It is interesting

to see that the derived result remains consistent and, if anything, statistical precision increases.

It is likely that a more detailed measure of institutional constraints, in which 20 degrees of

democracy are possible, contributes to a more precise estimation of the underlying relationship.

Note also that the corresponding results for development assistance and oil rents remain robust.13

13Incorporating natural resource rents instead of oil rents produces the same conclusion.

18

Table 2: OLS regression results from extensions, estimating the number of deaths from terror-ism in country i and year t.

IV regressionsb

(1) (2) (3) (4) (5) (6)

Dependent variable: Ln(1+deaths from terrorism)

EXEC 0.318∗ 0.298∗ 0.377∗ 0.355(0.172) (0.175) (0.212) (0.221)

(EXEC)2 -0.040∗ -0.038∗ -0.052∗ -0.051∗

(0.021) (0.022) (0.027) (0.028)

Natural resource rents 0.761∗∗∗ 0.522∗

in US$ 10,000/cap (0.279) (0.304)

Development assistance 12.701∗∗ 12.729∗∗ 10.450∗∗ 15.266∗ 16.049∗ 13.056∗∗

in US$ 10,000/cap (6.082) (6.146) (4.777) (8.879) (9.174) (6.404)

Oil rents 0.761∗∗ 0.619∗∗ 0.648∗∗ 0.355in US$ 10,000/cap (0.299) (0.252) (0.290) (0.290)

Polity IV 0.149∗∗ 0.247∗∗∗

(0.065) (0.086)

(Polity IV)2 -0.007∗∗ -0.012∗∗∗

(0.003) (0.004)

Control variablesa yes yes yes yes yes yes

Country-fixed effects yes yes yes yes yes yes

Year-fixed effects yes yes yes yes yes yes

Continent-specific time trends yes yes yes



p < 0.01. aIncludes GDP/capita, population size, and growth rate. bIn column (4), EXEC, (EXEC)2, natural

resource rents, and development assistance are instrumented by their respective lagged values in the previous

year. Columns (5) and (6) apply the same logic for EXEC, (EXEC)2, oil rents, development assistance, Polity

IV and (Polity IV)2. In all estimations, Shea’s partial R2 ranges between 0.55 and 0.78, leaving little concern

about potentially weak instruments.

19

Finally, I conduct robustness checks using alternative measures for the dependent variable. In

particular, the benchmark results are consistent when employing a measure of deaths per capita

or estimating a more traditional binary indicator of 25 or more casualties. The corresponding

results are referred to the appendix Table AV.14

Finally, columns (4) to (6) display results from instrumental variable regressions, addressing

potential reverse causality concerns. For example, it is possible that pressure from terrorism in

turn affects the institutional equilibrium of a country, the degree of resource extraction, or the

level of international assistance. If that were the case, the coefficients derived in Table 1 could

be biased.15

To alleviate such concerns, I follow recent macroeconomic studies in using lagged values

of the variables of interest as instruments. In particular, the growth literature has resorted

to this technique (e.g., Temple, 1999; Schularick and Steger, 2010; Mirestean and Tsangarides,

2016), as well as studies analyzing effects from democracy (Bhattacharyya and Hodler, 2010) and

corruption (Arezki and Bruckner, 2011). This IV-approach, in combination with the theoretical

intuition provided in section 3 and the fixed-effects framework, should mitigate endogeneity

concerns.

Note that the derived coefficients in columns (4) to (6) are largely in line with the benchmark

OLS results from Table 1.16 In terms of statistical power, executive constraints turn marginally

insignificant on conventional levels (t-statistic of 1.61) in column (5), yet inflated standard errors

are likely to blame. In terms of magnitude, the coefficient associated with EXEC remains strong

and even rises (from 0.308 in column (6), Table 1, to 0.355).

Further, the importance of R prevails throughout the IV-estimations, with the exception of

oil rents in column (6). Nevertheless, a quantitative interpretation of the derived coefficient still

suggests a positive relationship between oil rents and terrorism. Finally, employing the polity2

14As regressions are estimated in a fixed-effects framework, I refrain from using a logit or probit approach toestimate the binary outcome variable of conflict incidence, but rather employ a conventional OLS approach, as iscommon in the literature (see Greene, 2004).

15Note that finding an instrumental variable for any given country on the yearly level provides a difficult task.Large country-specific shocks, such as colonialism or geography, are unsuitable candidates in a panel framework,as they provide no within-country variation. Other prominent candidates, such as natural disasters, have beenshown to directly affect income levels and conflict incidence, rendering them invalid for the present estimation.

16Testing for weak instruments confirms the validity of the lagged instruments, as Shea’s partial R2 statisticproduces values between 0.55 and 0.78 (see Shea, 1997).

20

variable produces a result that is consistent with the findings from OLS regressions. Overall,

these IV estimations confirm the findings derived in Table 1.

5.3 Onset of Terrorism

From terrorist incidence, I now move to probit regressions, predicting terrorist onset in Table

3 (displaying marginal effects). As before, a linear term is not sufficient to accurately describe

the underlying relationship, but column (2) produces the familiar nonlinearity once a quadratic

term is added. Note that the entire sample “only” produces 560 country-year observations in

which terrorism occurs, but has not occurred the year before. On average, this corresponds

to approximately 3.6 observations per country, indicating that incorporating fixed effects may

not leave sufficient statistical variation to reveal the underlying dynamics. (Nevertheless, a

fixed-effects framework produces the same quantitative conclusions, consistent with the results

displayed in Table 3.17)

The regression shown in column (3) includes the familiar control variables, in addition to

natural resource rents and development assistance. Consistent with the findings related to ter-

rorism incidence, development assistance emerges as a positive predictor. Although the measure

for natural resource rents barely misses the conventional hurdle of statistical significance (t-value

of 1.61), a sizeable positive coefficient is established. In addition, the familiar inverted U-shape

for the effect of institutional constraints on terrorism onset prevails.

Column (4) substitutes oil rents for overall natural resources, and we find the familiar positive

relationship to terrorism. Further, column (5) turns to the alternative measure for institutional

constraints by employing the polity2 variable. As before, the corresponding findings support all

predictions related to σ and R.

Finally, columns (6) to (8) display results from IV regressions, following the same sequence

as columns (4) to (6) in Table 2. It is reassuring to see that all suggested relationships re-

ceive strong support. Figure 4 plots the underlying relationships for executive constraints and

development assistance, using the results from column (3) as a reference point. Compared to

authoritarianism, terrorism becomes approximately seven percentage points more likely if con-

17When including country-fixed effects, EXEC produces a coefficient of 0.210 (standard error 0.114), whereas(EXEC)2 produces a coefficient of -0.022 (0.014). Natural resource rents produce a coefficient of 0.007 (0.004).

21

Tab

le3:

Res

ult

sfr

omp

rob

itre

gre

ssio

n,

esti

mat

ing

the

onse

tof

terr

oris

min

cou

ntr

yi

and

yeart.

Dis

pla

yin

gm

argi

nal

effec

ts.

IVb

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Depen

den

tvariable:

Onsetofterrorism

EX

EC

0.0

04

0.0

61∗∗∗

0.0

42∗∗∗

0.0

40∗∗

0.2

14∗∗∗

0.2

03∗∗∗

(0.0

02)

(0.0

14)

(0.0

15)

(0.0

16)

(0.0

72)

(0.0

74)

(EX

EC

)2-0

.007∗∗∗

-0.0

04∗∗

-0.0

04∗∗

-0.0

21∗∗

-0.0

20∗∗

(0.0

02)

(0.0

02)

(0.0

02)

(0.0

09)

(0.0

09)

Natu

ral

reso

urc

ere

nts

0.0

61

0.3

05∗

0.3

10∗∗

inU

S$

10,0

00/ca

p(0

.038)

(0.1

75)

(0.1

31)

Dev

elopm

ent

ass

ista

nce

1.3

79∗∗

1.3

47∗∗

1.6

74∗∗∗

8.5

68∗∗∗

7.9

34∗∗∗

inU

S$

10,0

00/ca

p(0

.538)

(0.5

52)

(0.5

59)

(2.6

70)

(2.7

37)

Oil

rents

0.0

74∗

0.1

02∗∗∗

0.3

56∗

inU

S$

10,0

00/ca

p(0

.041)

(0.0

39)

(0.1

88)

Polity

IV0.0

31∗∗∗

0.1

48∗∗∗

(0.0

05)

(0.0

24)

(Polity

IV)2

-0.0

01∗∗∗

-0.0

06∗∗∗

(0.0

00)

(0.0

01)

Contr

ol

vari

able

sayes

yes

yes

yes

yes

yes

#of

countr

ies

156

156

134

134

134

133

133

155

N3,6

78

3,6

78

2,9

29

2,8

06

2,8

04

2,9

08

2,7

41

3,6

55

Pse

udoR

2(M

cFadden

)0.0

01

0.0

06

0.0

60

0.0

57

0.0

71

Notes:

Robust

standard

erro

rsare

dis

pla

yed

inpare

nth

eses

.∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1.

aIn

cludes

GD

P/ca

pit

a,

popula

tion

size

,and

gro

wth

rate

.bIn

colu

mn

(4),

EX

EC

,(E

XE

C)2

,natu

ral

reso

urc

ere

nts

,and

dev

elopm

ent

ass

ista

nce

are

inst

rum

ente

dby

thei

rre

spec

tive

lagged

valu

esin

the

pre

vio

us

yea

r.C

olu

mns

(5)

and

(6)

apply

the

sam

elo

gic

for

EX

EC

,(E

XE

C)2

,oil

rents

,dev

elopm

ent

ass

ista

nce

,P

olity

IVand

(Polity

IV)2

.In

all

esti

mati

ons,

Shea

’spart

ial

R2

ranges

bet

wee

n0.5

5and

0.7

8,

leav

ing

litt

leco

nce

rnab

out

pote

nti

ally

wea

kin

stru

men

ts.

22

straints on the executive are measured at a value of 4.8. Further, even perfect democracies are

more likely to suffer from terrorism – a result that is consistent with the theoretical priors and

the empirical results from considering the incidence of terrorism.

Constraints on the executive Development assistance per capita

46

810

% c

hang

e in

like

lihoo

d of

terr

oris

m o

nset

0 2 4 6 8Constraints on executive

05

1015

% c

hang

e in

like

lihoo

d of

terr

oris

m o

nset

0 .02 .04 .06 .08 .1Development assistance in $10,000 per capita

Figure 4: Effect of constraints on the executive and development assistance on the onset ofterrorism, plotting results from column (3) in Table 3.

Related to development assistance per capita, a one standard deviation increase (US$ 117

per capita) relates to a 1.6 percentage point rise in the probability of experiencing terrorism.

In the extreme case, moving from US$ 0 to US$ 1,845 (Jordan in 1979), the onset of terrorism

becomes 25.4 percentage points more likely.

6 Empirical Analysis of Conflicts

After terrorism, I now move to the analysis of domestic conflicts. Recall that the model predicts

a linear negative relationship between constraints on the executive and the incidence and onset

of civil conflict.

6.1 Incidence of Conflicts: Main Results

Table 4 follows the same sequence as analyzing the incidence of terrorism, beginning with a

univariate regression. Indeed, we find a negative link between institutional constraints and the

number of victims from internal conflicts. In terms of magnitude, raising executive constraints

by one level (say, from two to three) is associated with a 12.6 percentage point decrease in

23

the number of conflict casualties. Column (2) shows that this relationship is not quadratic,

in contrast to the relationship with terrorism – a result that is consistent with the theoretical

predictions.

Table 4: OLS regression results, estimating the number of deaths from conflicts in country iand year t.

(1) (2) (3) (4) (5) (6)

Dependent variable: Ln(1+deaths from internal conflict)

EXEC -0.126∗∗ 0.054 -0.145∗∗ -0.108∗ -0.109∗ -0.112∗

(0.061) (0.301) (0.062) (0.058) (0.062) (0.062)

(EXEC)2 -0.021(0.034)

Natural resource rents 0.456 0.507 0.519in US$ 10,000/cap (0.322) (0.363) (0.423)

Country-fixed effects yes yes yes yes

Control variablesa yes yes yes

Year-fixed effects yes yes

Continent-specific time trends yes



p < 0.01. aIncludes GDP/capita, population size, and growth rate.

Columns (3) to (6) then incorporate country-fixed effects, natural resource rents, the famil-

iar control variables, year-fixed effects, and continent-specific time trends. However, executive

constraints remain a negative predictor of conflict incidence with the respective coefficient only

fluctuating marginally between -0.11 and -0.15. In addition, natural resource rents do not play

any role, consistent with findings from Elbadawi and Sambanis (2002) or Fearon and Laitin

(2003). Remember that according to the profit-maximizing decision by the opposition group

24

modeled in section 3 we should not expect a particularly strong relationship between R and

conflicts, but rather executive constraints should be the dominant factor.

6.2 Incidence of Conflicts: Extensions

As with the analysis of terrorism, I now move to several extensions, displayed in Table 5.

Following the same sequence as in Table 3, columns (1) and (2) consider alternative definitions

of R by incorporating foreign development assistance and oil rents. However, none of these

aspects are closely related to the incidence of national conflict. The negative effect from EXEC,

however, prevails.

Column (3) switches to the polity2 variable as an alternative measure for institutional con-

trols and, as with the analysis of terrorism, the initial result is confirmed. As before, the

more flexible measure of the polity2 variable brings out the underlying relationship with more

statistical precision, as the associated level of statistical significance increases to five percent.

Nevertheless, development assistance and oil rents remain largely irrelevant. In alternative es-

timations, I also address the measurement of the dependent variable. In particular, all results

are preserved when employing a measure for deaths per capita or using a binary indicator for

experiencing 25 or more deaths, which represents a more traditional way of measuring conflict

incidence. These results are referred to the appendix Table AV.18

Finally, columns (4) to (6) re-estimate the corresponding regressions in the familiar IV set-

ting, where executive constraints, natural resource rents, development assistance, oil rents, and

the polity2 variable are instrumented by their lagged values from the previous year. The results

further support the hypotheses, as institutional constraints remain important, but measures for

R do not.

In terms of magnitude, an increase in the level of executive constraints by one point is

associated with a decrease in the number of deaths from conflict by 11 to 13.6 percentage

points, depending on which regression we choose from Table 5. It is also noteworthy to point out

that the corresponding regressions are able to explain approximately 60 percent of the observed

variation in deaths from conflicts throughout the sample, as indicated by the respective adjusted

18Since all regressions are estimated in a fixed-effects framework, I refrain from using a logit or probit approachto estimate the binary outcome variable of conflict incidence, but rather employ a conventional OLS approach,as is common in the literature (see Greene, 2004).

25

Table 5: OLS regression results from extensions, estimating the number of deaths from conflictsin country i and year t.

IV regressionsb

(1) (2) (3) (4) (5) (6)

Dependent variable: Ln(1+deaths from internal conflict)

EXEC -0.115∗ -0.112∗ -0.136∗∗ -0.135∗∗

(0.063) (0.063) (0.068) (0.066)

Natural resource rents 1.264∗ 0.992in US$ 10,000/cap (0.709) (0.794)

Development assistance -2.787 -2.401 -7.307 5.584 5.929 -2.214in US$ 10,000/cap (7.628) (7.585) (8.338) (13.829) (14.257) (14.496)

Oil rents 1.265 1.164 0.966 0.814in US$ 10,000/cap (0.858) (0.827) (0.908) (0.878)

Polity IV -0.050∗∗ -0.056∗∗

(0.023) (0.025)

Control variablesa yes yes yes yes yes yes

Country-fixed effects yes yes yes yes yes yes

Year-fixed effects yes yes yes yes yes yes

Continent-specific time trends yes yes yes



p < 0.01. aIncludes GDP/capita, population size, and growth rate. bIn column (4), EXEC, natural resource

rents, and development assistance are instrumented by their respective lagged values in the previous year.

Columns (5) and (6) apply the same logic for EXEC, oil rents, development assistance, and Polity IV. In all

estimations, Shea’s partial R2 ranges between 0.27 and 0.78, leaving little concern about potentially weak

instruments.

26

R2 values.

6.3 Onset of Conflicts

Finally, Table 6 turns to the onset of domestic conflicts. For this measure, the statistical variation

throughout the sample diminishes substantially, as conflict has begun in “only” 96 country-year

observations, where the respective country has not suffered from conflict in the preceding year.

In fact, (luckily) only 51 countries appear on this list.

Table 6: Results from probit regressions, estimating the onset of conflict in country i and yeart. Displaying marginal effects.

IV regressionsb

(1) (2) (3) (4) (5) (6) (7)

EXEC -0.010∗∗∗ -0.007∗∗∗ -0.006∗∗∗ -0.066∗∗ -0.060∗∗

(0.002) (0.002) (0.002) (0.029) (0.029)

Natural resource rents 0.027∗ 0.363∗ 0.139in US$ 10,000/cap (0.015) (0.200) (0.165)

Development assistance 0.453 0.507 0.133 4.804 6.012in US$ 10,000/cap (0.654) (0.655) (0.663) (11.158) (11.101)

Oil rents 0.039∗ 0.044∗∗ 0.508∗

in US$ 10,000/cap (0.020) (0.019) (0.259)

Polity IV -0.001∗ -0.012(0.001) (0.008)

Control variablesa yes yes yes yes yes

# of countries 151 129 129 128 127 127 150N 2,918 2,268 2,220 2,205 2,239 2,200 2,873Pseudo R2 (McFadden) 0.046 0.093 0.092 0.088

Notes: Robust standard errors are displayed in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. aIncludes

GDP/capita, population size, and growth rate. bIn column (5), EXEC, natural resource rents, and development

assistance are instrumented by their respective lagged values in the previous year. Columns (6) and (7) apply the

same logic for EXEC, oil rents, development assistance, Polity IV, and natural resource rents. In all estimations,

Shea’s partial R2 ranges between 0.65 and 0.93, leaving little concern about potentially weak instruments.

Column (1) displays results from a univariate regression, suggesting that the onset of conflict

is less likely for inclusive institutions. This result is confirmed once additional control variables

are included, but the related magnitude decreases to 0.007. Note that natural resource abun-

27

dance is associated with a higher likelihood of conflict onset, something that is not necessarily

suggested by the model, but has been proposed by previous studies (Collier and Hoeffler, 1998,

Koubi et al., 2014).19 Thus, it is possible that further dynamics related to natural resources

relate to civil conflicts.

Further, columns (3) and (4) explore different measures for σ and R. Concerning institutional

constraints, the negative link to conflict is confirmed. Related to available rents, we do confirm

the importance of oil rents in driving up the likelihood of conflict. Again, this indicates that

natural resources could exhibit an additional dynamic when it comes to violent conflicts. In

particular, the results related to natural resources and specifically oil confirm findings by Collier

and Hoeffler (2004), Humphreys (2005), and Ross (2006).

Finally, columns (5) to (7) estimate the familiar sequence of IV regressions. Most impor-

tantly, executive controls remain a powerful predictor of conflict onset and the associated co-

efficient increases more than ten-fold, from -0.006 to -0.066. This indicates that applying a

regular probit framework would underestimate the effect of executive constraints on the onset

of conflict. Intuitively, it is possible that an outbreak of open conflict leads to a tightening of

institutional controls, as the ruling government tries to maintain control. Such dynamics would

make it difficult to isolate the true effect of institutional controls on the onset of conflict in a

standard OLS framework. However, employing executive constraints in the preceding (peaceful)

year as an instrument for contemporaneous executive controls circumvents this problem and is

likely better able to reveal the underlying effect of executive constraints on conflict onset.

In addition, increased oil rents continue to predict conflict onset, whereas foreign development

assistance remains an irrelevant factor. Finally, employing the Polity2 variable as an alternative

estimate for institutional controls confirms the negative impact on conflict onset, but the derived

coefficient fails to clear the conventional levels of statistical significance. It is important to note

that the coefficient becomes stronger in quantitative terms, but standard errors are inflated

substantially (from 0.001 to 0.008). In fact, we observe substantially elevated standard errors

for all derived coefficients in the IV regressions – a result that is likely driven by less statistical

variation in the employed instruments and the limited number of observations in which conflict

19Nillesen and Bulte (2014) provide a recent summary of the link between natural resources and violent conflict.Also, van der Ploeg (2011) provides an overview of how natural resources can affect development in various ways.

28

emerges (96).

7 Conclusion

This paper analyzes the decision of an opposition group to pursue a campaign of organized

violence against a ruling government. Depending on constraints on the executive (i.e., the

inclusiveness of political institutions) and the availability of rents, the group can choose between

peace, terrorism, and open civil conflict. An important distinction between open conflict and

terrorism comes from comparing the associated costs and benefits. Contrary to civil conflict,

both parameters are naturally limited for a terrorist campaign.

Analyzing the opposition group’s optimal choice then reveals that terrorism becomes likely

if executive constraints are intermediate and rents are high. In fact, even in largely inclusive

institutions terrorism remains a viable option, but only if rents are considerable. Civil conflict

emerges as the dominant option if executive constraints are particularly poor, whereas peace

becomes the likely outcome under high executive constraints and a modest to low availability of

rents.

Taking these hypotheses to the data, the paper analyzes 5,400 and 3,586 country-year ob-

servations for terrorism and conflicts, respectively. Employing country- and year-fixed effects,

continent-specific time trends, and the conventional time-variant control variables, the theo-

retical predictions receive firm support. Intermediate ranges of executive control increase the

number of deaths from terrorism and the likelihood of terrorism onset. However, less constraints

on the executive likely pushes the country into civil conflict.

Further, large available rents increase the incidence of terrorism, as well as the likelihood of

terrorism onset. For all three rent measures (natural resource rents, oil rents, and development

assistance), a one standard deviation increase in per capita revenue is suggested to increase the

number of deaths from terrorism by approximately 15 to 16 percentage points. It is remark-

able how consistent this magnitude remains for all three measures and across several different

estimations.

Related to domestic conflicts, tighter controls on the executive substantially decrease the

number of casualties in a linear fashion. Moving from a totally authoritarian regime to perfectly

29

inclusive institutions is associated with a decrease in the number of battle-related deaths by

approximately 74 percentage points. Considering conflict onset, results are less precise in statis-

tical terms – an artifact that becomes less surprising once we are reminded of the rare occurrence

of conflict onset (96 observations in 51 countries). Nevertheless, the corresponding coefficients

consistently confirm the notion that executive constraints are negatively tied to conflict onset

in a linear way.

To my knowledge, this paper is among the first to jointly analyze the profit-maximizing

decision of an opposition group between peace, terrorism, and open civil conflict. The theoretical

motivation is basic and one could think of several extensions. Nevertheless, the empirical part

of the paper shows that the model’s simple predictions are consistently observed in global data,

even when controlling for a number of potentially confounding factors and fixed effects. As

such, the paper may serve as a starting point to better understand and anticipate the decisions

of opposition groups, integrating the drivers of terrorism and national conflicts.

30

References

Abadie, A. (2006). Poverty, political freedom, and the roots of terrorism. American EconomicReview Papers & Proceedings, 96(2):50–56.

Acemoglu, D., Johnson, S., and Robinson, J. A. (2005). Institutions as a fundamental cause oflong-run growth. Handbook of Economic Growth, 1:385–472.

Acemoglu, D., Johnson, S., Robinson, J. A., and Yared, P. (2008). Income and democracy.American Economic Review, 98(3):808–842.

Acemoglu, D., Vindigni, A., and Ticchi, D. (2010). Persistence of civil wars. Journal of theEuropean Economic Association, 8(2-3):664–676.

Acemoglu, D. and Wolitzky, A. (2014). Cycles of conflict: An economic model. The AmericanEconomic Review, 104(4):1350–1367.

Arezki, R. and Bruckner, M. (2011). Oil rents, corruption, and state stability: Evidence frompanel data regressions. European Economic Review, 55(7):955–963.

Besley, T. and Persson, T. (2009). Repression or civil war? American Economic Review Papers& Proceedings, 99(2):292–297.

Besley, T. and Persson, T. (2011). The logic of political violence. The Quarterly Journal ofEconomics, 126(3):1411–1445.

Bhattacharyya, S. and Hodler, R. (2010). Natural resources, democracy and corruption. Euro-pean Economic Review, 54(4):608–621.

Blattman, C. and Miguel, E. (2010). Civil war. Journal of Economic Literature, 48(1):3–57.

Blomberg, S. B., Hess, G. D., and Weerapana, A. (2004). Economic conditions and terrorism.European Journal of Political Economy, 20(2):463–478.

Cervellati, M., Jung, F., Sunde, U., and Vischer, T. (2014). Income and democracy: Comment.The American Economic Review, 104(2):707–719.

Chenoweth, E. (2013). Terrorism and democracy. Annual Review of Political Science, 16:355–378.

CIA (2016). CIA World Factbook. The World Factbook. Retrieved fromhttps://www.cia.gov/library/publications/the-world-factbook/.

Clutterbuck, R. (1992). Negotiating with terrorists. Terrorism and Political Violence, 4(4):263–287.

Collier, P. and Hoeffler, A. (1998). On economic causes of civil war. Oxford Economic Papers,50(4):563–573.

Collier, P. and Hoeffler, A. (2004). Greed and grievance in civil war. Oxford Economic Papers,56(4):563–595.

31

Collier, P., Hoeffler, A., and Rohner, D. (2009). Beyond greed and grievance: Feasibility andcivil war. Oxford Economic Papers, 61(1):1–27.

Cotet, A. M. and Tsui, K. K. (2013). Oil and conflict: What does the cross country evidencereally show? American Economic Journal: Macroeconomics, 5(1):49–80.

Dixon, J. (2009). What causes civil wars? Integrating quantitative research findings. Interna-tional Studies Review, 11(4):707–735.

Do, Q.-T. and Iyer, L. (2010). Geography, poverty and conflict in Nepal. Journal of PeaceResearch, 47(6):735–748.

Dolnik, A. and Fitzgerald, K. M. (2011). Negotiating hostage crises with the new terrorists.Studies in Conflict & Terrorism, 34(4):267–294.

Elbadawi, I. and Sambanis, N. (2002). How much war will we see? Explaining the prevalenceof civil war. Journal of Conflict Resolution, 46(3):307–334.

Enders, W. and Hoover, G. A. (2012). The nonlinear relationship between terrorism and poverty.American Economic Review Papers & Proceedings, 102(3):267–272.

Esteban, J., Mayoral, L., and Ray, D. (2012). Ethnicity and conflict: An empirical study.American Economic Review, 102(4):1310–42.

Esteban, J. and Ray, D. (2008). On the salience of ethnic conflict. American Economic Review,98(5):2185–2202.

Esteban, J. and Ray, D. (2011). Linking conflict to inequality and polarization. The AmericanEconomic Review, 101(4):1345–1374.

Fearon, J. D. and Laitin, D. D. (2003). Ethnicity, insurgency, and civil war. American PoliticalScience Review, 97(01):75–90.

Findley, M. G. and Young, J. K. (2012). Terrorism and civil war: A spatial and temporalapproach to a conceptual problem. Perspectives on Politics, 10(02):285–305.

Greene, W. (2004). The behaviour of the maximum likelihood estimator of limited dependentvariable models in the presence of fixed effects. The Econometrics Journal, 7(1):98–119.

Group, W. B. (2012). World Development Indicators 2012. World Bank Publications.

Hegre, H. (2014). Democracy and armed conflict. Journal of Peace Research, 51(2):159–172.

Hegre, H. and Sambanis, N. (2006). Sensitivity analysis of empirical results on civil war onset.Journal of Conflict Resolution, 50(4):508–535.

Humphreys, M. (2005). Natural resources, conflict, and conflict resolution: Uncovering themechanisms. Journal of Conflict Resolution, 49(4):508–537.

Islam, N. (1995). Growth empirics: A panel data approach. The Quarterly Journal of Economics,110(4):1127–1170.

32

Koubi, V., Spilker, G., Bohmelt, T., and Bernauer, T. (2014). Do natural resources matter forinterstate and intrastate armed conflict? Journal of Peace Research, 51(2):227–243.

Krueger, A. B. and Maleckova, J. (2003). Education, poverty and terrorism: Is there a causalconnection? Journal of Economic Perspectives, 17(4):119–144.

LaFree, G. and Dugan, L. (2007). Introducing the Global Terrorism Database. Terrorism andPolitical Violence, 19(2):181–204.

Lessing, B. (2015). Logics of violence in criminal war. Journal of Conflict Resolution, 59(8):1486–1516.

Marshall, M. G. and Jaggers, K. (2002). Polity IV project: Political regime characteristics andtransitions, 1800-2002.

Miguel, E., Satyanath, S., and Sergenti, E. (2004). Economic shocks and civil conflict: Aninstrumental variables approach. Journal of Political Economy, 112(4):725–753.

Mirestean, A. and Tsangarides, C. G. (2016). Growth determinants revisited using Limited-Information Bayesian Model Averaging. Journal of Applied Econometrics, 31(1):106–132.

Neumann, P. R. (2007). Negotiating with terrorists. Foreign Affairs – New York, 86(1):128.

Nielsen, R. A., Findley, M. G., Davis, Z. S., Candland, T., and Nielson, D. L. (2011). Foreign aidshocks as a cause of violent armed conflict. American Journal of Political Science, 55(2):219–232.

Nillesen, E. and Bulte, E. (2014). Natural resources and violent conflict. Annu. Rev. Resour.Econ., 6(1):69–83.

Nunn, N. and Qian, N. (2014). US food aid and civil conflict. The American Economic Review,104(6):1630–1666.

Reynal-Querol, M. and Montalvo, J. G. (2005). Ethnic polarization, potential conflict and civilwar. American Economic Review, 95(3):796–816.

Ross, M. (2006). A closer look at oil, diamonds, and civil war. Annu. Rev. Polit. Sci., 9:265–300.

Schularick, M. and Steger, T. M. (2010). Financial integration, investment, and economic growth:Evidence from two eras of financial globalization. The Review of Economics and Statistics,92(4):756–768.

Schutte, S. (2015). Geography, outcome, and casualties: A unified model of insurgency. Journalof Conflict Resolution, 59(6):1101–1128.

Shea, J. (1997). Instrument relevance in multivariate linear models: A simple measure. Reviewof Economics and Statistics, 79(2):348–352.

START (2015). Global terrorism database. National Consortium for the Study of Terrorismand Responses to Terrorism (START). Retrieved from http://www.start.umd.edu/gtd.

33

Temple, J. (1999). The new growth evidence. Journal of Economic Literature, 37(1):112–156.

UCDP (2015). UCDP battle-related deaths dataset v.5-2015, Uppsala Conflict Data Program.www.ucdp.uu.se, Uppsala University.

van der Ploeg, F. (2011). Natural resources: Curse or blessing? Journal of Economic Literature,49(2):366–420.

Weidmann, N. B. and Ward, M. D. (2010). Predicting conflict in space and time. Journal ofConflict Resolution, 54(6):883–901.

34

Table AI: Payoff structure, modelling the incumbent’s and opposition’s options.

Incumbent

σL σH

Opposition

Civil conflict βR(1− σL

), βR

(1− σH

),(

1− σL)(

1− β)R

(1− σH

)(1− β

)R

Terrorism σLR+ β(12 − σL)R− c, σHR+ β(1

2 − σH)R− c,(1− σL)R− β(1

2 − σL)R (1− σH)R− β(12 − σH)R

Peace σLR, σHR,

(1− σL)R (1− σH)R

Appendix

The Government’s Choice

It is straightforward to show that the government always chooses the lowest value of σ possible,

given constitutional constraints. To see this, suppose the incumbent group can choose between

σL, the minimal share dedicated to the opposition as per institutional constraints, and σH (with

0 ≤ σL < σH). In that case, Table AI displays the corresponding payoff matrix.

From the incumbent’s perspective, σL constitutes the dominant strategy. In the case of

war, the incumbent’s income is higher for σL, since σL < σH . In the case of terrorism, the

corresponding payoff is larger for σL. Specifically, for terrorism: (1 − σL)R − β(12 − σL)R >

(1 − σH)R − β(12 − σH)R, which, after collecting terms, produces σH > σL. Finally, in the

peaceful scenario, the incumbent’s income from setting σL also dominates, as comparing the

respective payoffs again produces σH > σL.

35

Sam

ple

Cou

ntr

ies

Tab

leA

II:

Cou

ntr

ies

incl

ud

edin

terr

oris

man

dco

nfl

ict

sam

ple

wit

hre

spec

tive

nu

mb

erof

annu

alob

serv

atio

ns.

Countr

yT

err

ori

smC

onfl

ict

Countr

yT

err

ori

smC

onfl

ict

Countr

yT

err

ori

smC

onfl

ict

Countr

yT

err

ori

smC

onfl

ict

Sam

ple

Sam

ple

Sam

ple

Sam

ple

Sam

ple

Sam

ple

Sam

ple

Sam

ple

Afg

hanis

tan

11

11

Dom

inic

an

Republic

43

25

Latv

ia18

18

Rw

anda

43

25

Alb

ania

29

25

Ecuador

43

25

Lebanon

24

25

Saudi

Ara

bia

42

25

Alg

eri

a43

25

Egypt,

Ara

bR

ep.

43

25

Leso

tho

43

25

Senegal

43

25

Arg

enti

na

43

25

El

Salv

ador

43

25

Lib

eri

a43

25

Serb

ia8

8A

rmenia

17

17

Equato

rial

Guin

ea

22

15

Lib

ya

14

14

Sie

rra

Leone

43

25

Aust

ralia

43

25

Eri

trea

18

19

Lit

huania

10

10

Slo

vak

Republi

c20

21

Aust

ria

43

25

Est

onia

18

18

Luxem

bourg

14

14

Slo

venia

18

18

Azerb

aij

an

16

16

Eth

iopia

31

25

Macedonia

,F

YR

20

21

Solo

mon

Isla

nds

22

23

Bahra

in32

25

Fij

i43

25

Madagasc

ar

43

25

South

Afr

ica

43

25

Bangla

desh

41

25

Fin

land

43

25

Mala

wi

43

25

Spain

43

25

Bela

rus

21

22

Fra

nce

43

25

Mala

ysi

a43

25

Sri

Lanka

43

25

Belg

ium

14

14

Gab

on

43

25

Mali

43

25

Sudan

33

Benin

43

25

Gam

bia

,T

he

43

25

Mauri

tania

43

25

Suri

nam

e37

25

Bhuta

n32

25

Georg

ia16

16

Mauri

tius

36

25

Sw

aziland

42

25

Bolivia

43

25

Germ

any

23

24

Mexic

o43

25

Sw

eden

43

25

Bots

wana

43

25

Ghana

43

25

Mold

ova

19

19

Sw

itzerl

and

32

25

Bra

zil

43

25

Gre

ece

43

25

Mongolia

31

25

Syri

an

Ara

bR

epublic

37

19

Bulg

ari

a32

25

Guate

mala

43

25

Monte

negro

88

Taji

kis

tan

16

16

Burk

ina

Faso

43

25

Guin

ea

26

25

Moro

cco

43

25

Tanzania

24

25

Buru

ndi

43

25

Guin

ea-B

issa

u39

25

Mozam

biq

ue

32

25

Thailand

43

25

Cab

oV

erd

e32

25

Guyana

43

25

Nam

ibia

23

24

Tim

or-

Lest

e2

2C

am

bodia

20

20

Hait

i15

15

Nepal

43

25

Togo

43

25

Cam

ero

on

43

25

Hondura

s43

25

Neth

erl

ands

43

25

Tri

nid

ad

and

Tobago

43

25

Canada

43

25

Hungary

21

22

New

Zeala

nd

35

25

Tunis

ia43

25

Centr

al

Afr

ican

Republic

43

25

India

43

25

Nic

ara

gua

43

25

Turk

ey

43

25

Chad

43

25

Indonesi

a43

25

Nig

er

43

25

Turk

menis

tan

77

Chile

43

25

Iran,

Isla

mic

Rep.

41

23

Nig

eri

a43

25

Uganda

30

25

Chin

a43

25

Iraq

30

12

Norw

ay

43

25

Ukra

ine

19

19

Colo

mbia

43

25

Irela

nd

42

25

Om

an

43

25

Unit

ed

Ara

bE

mir

ate

s37

25

Com

oro

s32

25

Isra

el

43

25

Pakis

tan

43

25

Unit

ed

Kin

gdom

43

25

Congo,

Dem

.R

ep.

41

22

Italy

43

25

Panam

a43

25

Unit

ed

Sta

tes

43

25

Congo,

Rep.

43

25

Jam

aic

a43

25

Papua

New

Guin

ea

38

25

Uru

guay

43

25

Cost

aR

ica

43

25

Japan

43

25

Para

guay

43

25

Uzb

ekis

tan

15

15

Cote

d’I

voir

e43

25

Jord

an

37

25

Peru

43

25

Venezuela

,R

B43

25

Cro

ati

a18

18

Kazakhst

an

21

22

Philip

pin

es

43

25

Vie

tnam

28

25

Cuba

42

25

Kenya

43

25

Pola

nd

22

23

Yem

en,

Rep.

20

21

Cypru

s37

25

Kore

a,

Rep.

43

25

Port

ugal

43

25

Zam

bia

43

25

Czech

Republic

20

21

Kuw

ait

18

18

Qata

r13

13

Zim

babw

e43

25

Denm

ark

43

25

Kyrg

yz

Republic

17

17

Rom

ania

22

23

Dji

bouti

22

23

Lao

PD

R28

25

Russ

ian

Federa

tion

21

22

36

Summary Statistics

Table AIII: Summary statistics for terrorism sample (1970 – 2014, excluding 1993 because ofdata unavailability). 5,400 observations unless indicated otherwise.

Variable Mean Min. Sourcea Description(Std. Dev.) (Max.)

Deaths from terrorism 43.79 0 GTD Number of deaths from terrorist attacks in country i and(261.29) (7,038) year t; applying ln(1+deaths)

EXEC 4.38 1 Polity IV Variable EXCONST , executive constraints,(2.31) (7) ranging from 1 to 7

Natural resource rents 0.06 0 WDI Total natural resource rents in US$ 10,000 per capitain US$ 10,000/cap (0.24) (4.63) (adjusted by GDP in constant 2005 prices and

population size); initially natural resource rents in % of GDP

GDP/capita 7,823 70 WDI GDP per capita in constant 2005 US$;(12,750) (87,773) applying Ln(GDP/capita)

Population size 3,909 23.29 WDI Total population size; applying Ln(GDP/capita)in 10,000 (13,370) (135,738)

Growth rate 2.03 -62.21 WDI GDP per capita growth(5.76) (104.66)

Polity IV 11.88 0 Polity IV Variable POLITY 2, re-scaled to run between 0(7.28) (20) (total autocracy) and 20 (total democracy); 5,366 observations

Development assis- 0.01 0 WDI Net official development assistance and official aidtance in US$10,000/cap

(0.01) (0.18) received (constant US$2005) in US$ 10,000 per capita

(adjusted by population size); 4,333 observations

Oil rents in 0.04 0 WDI Oil rents in US$ 10,000 per capita (adjusted by GDP inUS$ 10,000/cap (0.19) (4.53) constant 2005 prices and population size); initially oil

rents in % of GDP; 4,190 observations

Notes: aData come from the Global Terrorism Database (GTD), Polity IV, and the World Development Indicators

provided by the World Bank (WDI).

37

Table AIV: Summary statistics for conflict sample (1989 – 2014). 3,586 observations unlessindicated otherwise.

Variable Mean Min. Sourcea Description(Std. Dev.) (Max.)

Deaths from internal 149.13 0 UCDP Number of deaths from internal and internationalizedconflict (1,123) (49,698) internal conflicts in country i and year t; applying

ln(1+deaths)

EXEC 4.84 1 Polity IV Variable EXCONST , executive constraints,(2.09) (7) ranging from 1 to 7

Natural resource rents 0.07 0 WDI Total natural resource rents in US$ 10,000 per capitain US$ 10,000/cap (0.24) (3.29) (adjusted by GDP in constant 2005 prices and

population size); initially natural resource rents in% of GDP

GDP/capita 8,710 70 WDI GDP per capita in constant 2005 US$;(13,911) (87,773) applying Ln(GDP/capita)

Population size 4,102 32 WDI Total population size; applying Ln(population size)in 10,000 (14,107) (135,738)

Growth rate 2.18 -62.21 WDI GDP per capita growth(5.81) (104.66)

Polity IV 13.44 0 Polity IV Variable POLITY 2, re-scaled to run between 0(6.54) (20) (total autocracy) and 20 (total democracy); 3,551 observations

Development assis- 0.01 0 WDI Net official development assistance and official aidtance in US$10,000/cap

(0.01) (0.09) received (constant US$2005) in US$ 10,000 per capita

(adjusted by population size); 2,880 observations

Oil rents in 0.04 0 WDI Oil rents in US$ 10,000 per capita (adjusted by GDP inUS$ 10,000/cap (0.14) (1.91) constant 2005 prices and population size); initially oil

rents in % of GDP; 2,815 observations

Notes: aData come from the UCDP Battle-Related Deaths Dataset version 5.0-2015 (available under

http://www.pcr.uu.se/research/ucdp/datasets/ucdp_battle-related_deaths_dataset/), Polity IV, and the World

Development Indicators provided by the World Bank (WDI).

38

http://www.pcr.uu.se/research/ucdp/datasets/ucdp_battle-related_deaths_dataset/

Tab

leA

V:

OL

Sre

gres

sion

resu

lts

from

exte

nsi

ons,

esti

mat

ing

the

nu

mb

erof

dea

ths

from

terr

oris

man

dd

omes

tic

con

flic

tin

cou

ntr

yi

and

yea

rt.

Col

um

ns

(1)

thro

ugh

(4)

use

dea

ths

per

cap

ita

and

colu

mn

s(5

)th

rou

gh(8

)u

sea

bin

ary

ind

icato

rfo

r25

orm

ore

dea

ths

asth

ed

epen

den

tva

riab

le.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Dep

enden

tva

riable

:Ln( 1+d

eath

sfrom

terroris

mpopula

tion

)L

n( 1+d

eath

sfrom

dom

esticconflict

popula

tion

)25+

terr

ori

smdea

ths

(0/1)

25+

conflic

tdea

ths

(0/1)

EX

EC

0.3

18∗

0.2

98∗

-0.1

15∗

-0.1

12∗

0.0

61∗

0.0

59∗

-0.0

21∗∗

-0.0

21∗∗

(0.1

72)

(0.1

75)

(0.0

63)

(0.0

63)

(0.0

32)

(0.0

32)

(0.0

11)

(0.0

11)

(EX

EC

)2-0

.040∗

-0.0

38∗

-0.0

08∗∗

-0.0

08∗∗

(0.0

21)

(0.0

22)

(0.0

04)

(0.0

04)

Natu

ral

reso

urc

ere

nts

0.7

61∗∗∗

1.2

64∗

0.0

78∗

0.1

39

inU

S$

10,0

00/ca

p(0

.279)

(0.7

09)

(0.0

45)

(0.1

11)

Dev

elopm

ent

ass

ista

nce

12.7

01∗∗

12.7

29∗∗

-2.7

87

-2.4

01

1.9

77∗∗

1.9

89∗∗

-0.4

55

-0.3

93

inU

S$

10,0

00/ca

p(6

.082)

(6.1

46)

(7.6

28)

(7.5

85)

(0.8

51)

(0.8

62)

(1.3

77)

(1.3

71)

Oil

rents

0.7

61∗∗

1.2

65

0.0

76

0.1

18

inU

S$

10,0

00/ca

p(0

.299)

(0.8

58)

(0.0

48)

(0.1

34)

Contr

ol

vari

able

sayes

yes

yes

yes

yes

yes

yes

yes

Countr

y-fi

xed

effec

tsyes

yes

yes

yes

yes

yes

yes

yes

Yea

r-fixed

effec

tsyes

yes

yes

yes

yes

yes

yes

yes

Conti

nen

t-sp

ecifi

cyes

yes

yes

yes

yes

yes

yes

yes

tim

etr

ends

#of

countr

ies

136

136

136

136

136

136

136

136

N4,3

33

4,1

90

2,8

80

2,8

15

4,3

33

4,1

90

2,8

80

2,8

15

Adju

sted

R2

0.5

51

0.5

55

0.6

16

0.6

18

0.4

13

0.4

19

0.5

59

0.5

58

Notes:

Sta

ndard

erro

rscl

ust

ered

on

the

countr

yle

vel

are

dis

pla

yed

inpare

nth

eses

.∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1.

aIn

cludes

GD

P/ca

pit

a,

popula

tion

size

,and

gro

wth

rate

.

39

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